Введение. В условиях нарастания фунгицидной резистентности и климатических изменений разработка биологических методов защиты яровой пшеницы от листостебельных микозов становится особенно актуальной для аграрных регионов России. Хотя химические фунгициды доминируют в современных системах защиты, их эффективность снижается из-за развития резистентности у патогенов. При этом остаются недостаточно изученными региональные особенности формирования патогенного комплекса и эффективность комбинированных биопрепаратов на основе эндофитных бактерий с органическими кислотами для конкретных агроклиматических зон.
Цель. Исследовать многолетнюю динамику развития листостебельных микозов яровой пшеницы и оценить биологическую эффективность биопрепаратов на основе штаммов Bacillus mojavensis PS17 и Bacillus amyloliquefaciens KS-25 AU в комбинации с органическими кислотами в условиях Предкамья Республики Татарстан.
Материалы и методы. Для оценки динамики микозов, использовались данные фитопатологических учетов, проводимых в 2002-2025 гг. На сорте Ульяновская 105 изучалась обработка семян и опрыскивание биопрепаратом на основе Bacillus mojavensis PS17 при разных нормах его расхода. На сорте Экада 214 оценивалось применение экспериментальных биопрепаратов на основе Bacillus amyloliquefaciens KS-25 AU и различных органических кислот (аскорбиновая и янтарная).
Результаты. Установлено доминирование септориоза листьев в патогенном комплексе с превышением экономического порога вредоносности в период наблюдения. Наибольшая эффективность в контроле болезней достигнута при применении Bacillus mojavensis PS17 по схеме 1,0 л/т + 1,0 л/га: снижение развития септориоза на 68,6 и прибавка урожая 9,2%. Композиции на основе Bacillus amyloliquefaciens KS-25 AU с органическими кислотами при двукратном применении обеспечили снижение развития болезней на 32-77 и увеличение урожайности на 11,9-13,3%.
Заключение. Доказана высокая эффективность биопрепаратов на основе изученных штаммов эндофитных бактерий в сочетании с органическими кислотами для условий Предкамья Республики Татарстан. Результаты работы позволяют рекомендовать данные разработки для практического использования в региональных системах защиты яровой пшеницы как экологически безопасную альтернативу химическим фунгицидам.
Для цитирования: Абрамова А.А., Вафин И.Х., Медведев Н.А., Сафин Р.И. Совершенствование системы защиты яровой пшеницы с учетом мониторинга динамики листостебельных микозов и применения биопрепаратов в Республике Татарстан, Россия. Аграрный вестник Северного Кавказа. 2025;15(4):57-68. https://doi.org/10.31279/2949-4796-2025-15-4-57-68 EDN MONLPZ
To cite: Abramova A.A., Vafin I.Kh., Medvedev N.A., Safin R.I. Improving the spring wheat protection system through monitoring the dynamics of leaf and stem mycoses and application of biological preparations in the Republic of Tatarstan, Russia. Agrarian Bulletin of the North Caucasus. 2025;15(4):57-68. https://doi.org/10.31279/2949-4796-2025-15-4-57-68
Efficient use of cultivated areas is a necessary factor for sustainable development of agriculture and ensuring food security. Along with the rapid development of satellite technologies in developed countries, new methods are being searched for accurate and operational identification of cultivated areas. In this context, identification of cropland boundaries based on spectral analysis of data obtained from satellite images is considered one of the most optimal and accurate methods in modern agriculture. This article proposes a new approach to determine the suitability and green index of cultivated areas using satellite data obtained through the "Google Earth Engine" (GEE) platform. In this approach, two powerful algorithms, "SNIC (Simple Non-Iterative Clustering) Super Pixels" and "Canny Edge Detection Method", are combined. The SNIC algorithm combines pixels in a satellite image into larger regions (super pixels) with similar characteristics, thereby providing better image analysis. The Canny Edge Detection Method detects sharp changes (edges) in the image to determine the precise boundaries of agricultural fields. This study, carried out using high-resolution multispectral data from the Sentinel-2 satellite and the Google Earth Engine JavaScript API, has shown that the proposed method is effective in accurately and reliably classifying randomly selected agricultural fields. The combined use of these two tools allows for more accurate determination of the boundaries of agricultural fields by minimizing the effects of outliers in satellite images. As a result, more accurate and reliable maps can be created for agricultural monitoring and resource management over large areas based on the obtained data. By expanding the application capabilities of cloud-based platforms and artificial intelligence methods in the agricultural field.
Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing. While smart agriculture utilizes artificial intelligence (AI) technologies to enable precise control, it still encounters significant challenges, including excessive reliance on agricultural expert knowledge, difficulties in fusing multimodal data, poor adaptability to dynamic environments, and bottlenecks in real-time decision-making at the edge. Large language models (LLMs), with their exceptional capabilities in knowledge acquisition and semantic understanding, provide a promising solution to address these challenges. To this end, we propose Farm-LightSeek, an edge-centric multimodal agricultural IoT data analytics framework that integrates LLMs with edge computing. This framework collects real-time farmland multi-source data (images, weather, geographic information) via sensors, performs cross-modal reasoning and disease detection at edge nodes, conducts low-latency management decisions, and enables cloud collaboration for model updates. The main innovations of Farm-LightSeek include: (1) an agricultural "perception-decision-action" closed-loop architecture; (2) cross-modal adaptive monitoring; and (3)a lightweight LLM deployment strategy balancing performance and efficiency. Experiments conducted on two real-world datasets demonstrate that Farm-LightSeek consistently achieves reliable performance in mission-critical tasks, even under the limitations of edge computing resources. This work advances intelligent real-time agricultural solutions and highlights the potential for deeper integration of agricultural IoT with LLMs.
The Internet of Things (IoT) has revolutionized various applications including agriculture, but it still faces challenges in data collection and understanding. This paper proposes a real-time framework with three additional semantic layers to help IoT devices and sensors comprehend data meaning and source. The framework consists of six layers: perception, semantic annotation, interoperability, transportation, semantic reasoning, and application, suitable for dynamic environments. Sensors collect data in the form of voltage, which is then processed by microprocessors or microcontrollers in the semantic annotation and preprocessing layer. Metadata is added to the raw data, including the purpose, ID number, and application. Two semantic algorithms are proposed in the semantic interoperability and ontologies layer: the interoperability semantic algorithm for standardizing file types and the synonym identification algorithm for identifying synonyms. In the transportation layer, raw data and metadata are sent to other IoT devices or cloud computing platforms using techniques like WiFi, Zigbee networks, Bluetooth, and mobile communication networks. A semantic reasoning layer is proposed to infer new knowledge from the existing data, using fuzzy logic, Dempster-Shafer theory, and Bayesian networks. A Graphical User Interface (GUI) is proposed in the application layer to help users communicate with and monitor IoT sensors, devices, and new knowledge inferred. This framework provides a robust solution for managing IoT data, ensuring semantic completeness, and enabling real-time knowledge inference. The integration of uncertainty reasoning methods and semantic interoperability techniques makes this framework a valuable tool for advancing IoT applications in general and in agriculture in particular.
In precision agriculture and plant science, there is an increasing demand for wireless sensors that are easy to deploy, maintain, and monitor. This paper investigates a novel approach that leverages recent advances in extremely low-power wireless communication and sensing, as well as the rapidly increasing availability of unmanned aerial vehicle (UAV) platforms. By mounting a specialized wireless payload on a UAV, battery-less sensor tags can harvest wireless beacon signals emitted from the drone, dramatically reducing the cost per sensor. These tags can measure environmental information such as temperature and humidity, then encrypt and transmit the data in the range of several meters. An experimental implementation was constructed at AERPAW, an NSF-funded wireless aerial drone research platform. While ground-based tests confirmed reliable sensor operation and data collection, airborne trials encountered wireless interference that impeded successfully detecting tag data. Despite these challenges, our results suggest further refinements could improve reliability and advance precision agriculture and agrarian research.
The growing scale of modern farms has increased the need for efficient and adaptive multi-agent coverage strategies for pest, weed, and disease management. Traditional methods such as manual inspection and blanket pesticide spraying often lead to excessive chemical use, resource waste, and environmental impact. While unmanned aerial vehicles (UAVs) offer a promising platform for precision agriculture through targeted spraying and improved operational efficiency, existing UAV-based approaches remain limited by battery life, payload capacity, and scalability, especially in large fields where single-UAV or uniformly distributed spraying is insufficient. Although multi-UAV coordination has been explored, many current frameworks still assume uniform spraying and do not account for infestation severity, UAV dynamics, non-uniform resource allocation, or energy-efficient coordination. To address these limitations, this paper proposes a Density-Driven Optimal Control (D2OC) framework that integrates Optimal Transport (OT) theory with multi-UAV coverage control for large-scale agricultural spraying. The method supports non-uniform, priority-aware resource allocation based on infestation intensity, reducing unnecessary chemical application. UAVs are modeled as a linear time-varying (LTV) system to capture variations in mass and inertia during spraying missions. The D2OC control law, derived using Lagrangian mechanics, enables efficient coordination, balanced workload distribution, and improved mission duration. Simulation results demonstrate that the proposed approach outperforms uniform spraying and Spectral Multiscale Coverage (SMC) in coverage efficiency, chemical reduction, and operational sustainability, providing a scalable solution for smart agriculture.
Md. Samiul Basir, Samuel Noel, Dennis Buckmaster
et al.
Subsurface soil moisture is a primary determinant for root development and nutrient transportation in the soil and affects the tractability of agricultural vehicles. A statistical forecasting model, Vector AutoRegression (VAR), and a Long Short-Term Memory network (LSTM) were developed to forecast the subsurface soil moisture at a 20 cm depth using 9 years of historical weather data and subsurface soil moisture data from Fort Wayne, Indiana, USA. A time series analysis showed that the weather data and soil moisture have a stationary seasonal tendency and demonstrated that soil moisture can be forecasted from weather data. The VAR model estimates volumetric soil moisture of one-day ahead with an R<sup>2</sup>, MAE (m<sup>3</sup>m<sup>−3</sup>), MSE (m<sup>6</sup>m<sup>−6</sup>), and RMSE (m<sup>3</sup>m<sup>−3</sup>) of 0.698, 0.0561, 0.0046, and 0.0382 for 2021 corn cropping season, whereas the LSTM model using inputs of previous seven days yielded R<sup>2</sup>, MAE (m<sup>3</sup>m<sup>−3</sup>), MSE (m<sup>6</sup>m<sup>−6</sup>), and RMSE (m<sup>3</sup>m<sup>−3</sup>) of 0.998, 0.00237, 0.00002, and 0.00382, respectively as tested for cropping season of 2020 and 0.973, 0.00368, 0.00003 and 0.00577 as tested for the cropping season of 2021. The LSTM model presents a viable data-driven alternative to traditional statistical models for forecasting subsurface soil moisture.
The rice planthopper, Sogatella furcifera, is a piercing-sucking insect pest of rice, Oryza sativa. It is responsible for significant crop yield losses, and has developed moderate to high resistance to several commonly used chemical insecticides. We investigated the effects of the insect fungal pathogen Isaria javanica, alone and in combination with the chemical insecticide dinotefuran, on S. furcifera under both laboratory and field conditions. Our results show that I. javanica displays high infection efficiency and mortality for different stages of S. furcifera, reducing adult survival, female oviposition and ovary development. Laboratory bioassays showed that the combined use of I. javanica with a low dose (4–16 mg L–1) of dinotefuran resulted in higher mortality in S. furcifera than the use of I. javanica or dinotefuran alone. The combined treatment also had more significant effects on several host enzymes, including superoxide dismutase, catalase, peroxidase, and prophenol oxidase activities. In field trials, I. javanica effectively suppressed populations of rice planthoppers to low levels (22–64% of the level in untreated plots). Additional field experiments showed synergistic effects, i.e., enhanced efficiency, for the control of S. furcifera populations using the combination of a low dose of I. javanica (1×104 conidia mL–1) and a low dose of dinotefuran (~4.8–19.2% of normal field use levels), with control effects of >90% and a population level under 50 insects per 100 hills at 3–14 days post-treatment. Our findings indicate that the entomogenous fungus I. javanica offers an attractive biological control addition as part of the integrated pest management (IPM) practices for the control of rice plant pests.
The trends and reactions of the general public towards global events can be analyzed using data from social platforms, including Twitter. The number of tweets has been reported to help detect variations in communication traffic within subsets like countries, age groups and industries. Similarly, publicly accessible data and (in particular) data from social media about agricultural issues provide a great opportunity for obtaining instantaneous snapshots of farmer opinions and a method to track changes in opinion through temporal analysis. In this paper we hypothesize that the presence of keywords like precision agriculture, digital agriculture, Internet of Things (IoT), BigData, remote sensing, GPS, etc., in tweets could serve as an indicator of discussions centered around interest in modern farming practices. We extracted relevant tweets using keywords such as IoT, BigData and Geographical Information System (GIS), and then analyzed their geographical origin and frequency of their mention. We analyzed the Twitter data for the period of 1st -11th January 2018 to understand these trends and the factors affecting them. These factors, such as special events, projects, biogeography, etc., were further analyzed using tweet sources and trending hashtags from the database. The regions with the highest interest in the keywords were United States, Egypt, Brazil, Japan and China. A comparison of frequency of keywords revealed IoT as the most tweeted word (77.6%) in the downloaded data. The most used language was English followed by Spanish, Japanese and French. Periodical tweets on IoT from an account handled by IoT project on Twitter and Seminars on IoT in January in Santa Catarina (Brazil) were found to be the underlying factors for the observed trends.
Lars Nieradzik, Henrike Stephani, Jördis Sieburg-Rockel
et al.
In this study, we explore the explainability of neural networks in agriculture and forestry, specifically in fertilizer treatment classification and wood identification. The opaque nature of these models, often considered 'black boxes', is addressed through an extensive evaluation of state-of-the-art Attribution Maps (AMs), also known as class activation maps (CAMs) or saliency maps. Our comprehensive qualitative and quantitative analysis of these AMs uncovers critical practical limitations. Findings reveal that AMs frequently fail to consistently highlight crucial features and often misalign with the features considered important by domain experts. These discrepancies raise substantial questions about the utility of AMs in understanding the decision-making process of neural networks. Our study provides critical insights into the trustworthiness and practicality of AMs within the agriculture and forestry sectors, thus facilitating a better understanding of neural networks in these application areas.
This study addresses the underlying challenges of computer vision adoption in the Kenyan agricultural sector and how to solve these hurdles to commercialize this technology. Technological advancements have revolutionized the agriculture sector, where artificial intelligence enhances yields, mitigates losses, and manages natural resources, leading to increased productivity. Kenya is still lagging in the commercialization of computer vision to improve its agricultural sector, which is the largest source of GDP. Kenya has remarkable skills and expertise in artificial intelligence that can support artificial intelligence implementation; the government policies, data availability, and high cost incurred in starting a computer vision company are problematic. Through better government policies on subsidies and data, research and development investments, and AI forums, Kenya will solve the challenges of adopting computer vision. While computer vision has the potential to revolutionize the agricultural industry by improving crop yield, detecting diseases, and increasing efficiency, there are several barriers to its adoption, including inadequate infrastructure, lack of technical expertise, and limited funding. This study aims to identify the challenges hindering the implementation of computer vision technology in the Kenyan agricultural sector and propose potential solutions to address these challenges.
ObjectiveTo quickly and accurately assess the situation of crop lodging disasters, it is necessary to promptly obtain information such as the location and area of the lodging occurrences. Currently, there are no corresponding technical standards for identifying crop lodging based on UAV remote sensing, which is not conducive to standardizing the process of obtaining UAV data and proposing solutions to problems. This study aims to explore the impact of different spatial resolution remote sensing images and feature optimization methods on the accuracy of identifying wheat lodging areas.MethodsDigital orthophoto images (DOM) and digital surface models (DSM) were collected by UAVs with high-resolution sensors at different flight altitudes after wheat lodging. The spatial resolutions of these image data were 1.05, 2.09, and 3.26 cm. A full feature set was constructed by extracting 5 spectral features, 2 height features, 5 vegetation indices, and 40 texture features from the pre-processed data. Then three feature selection methods, ReliefF algorithm, RF-RFE algorithm, and Boruta-Shap algorithm, were used to construct an optimized subset of features at different flight altitudes to select the best feature selection method. The ReliefF algorithm retains features with weights greater than 0.2 by setting a threshold of 0.2; the RF-RFE algorithm quantitatively evaluated the importance of each feature and introduces variables in descending order of importance to determine classification accuracy; the Boruta-Shap algorithm performed feature subset screening on the full feature set and labels a feature as green when its importance score was higher than that of the shaded feature, defining it as an important variable for model construction. Based on the above-mentioned feature subset, an object-oriented classification model on remote sensing images was conducted using eCognition9.0 software. Firstly, after several experiments, the feature parameters for multi-scale segmentation in the object-oriented classification were determined, namely a segmentation scale of 1, a shape factor of 0.1, and a tightness of 0.5. Three object-oriented supervised classification algorithms, support vector machine (SVM), random forest (RF), and K nearest neighbor (KNN), were selected to construct wheat lodging classification models. The Overall classification accuracy and Kappa coefficient were used to evaluate the accuracy of wheat lodging identification. By constructing a wheat lodging classification model, the appropriate classification strategy was clarified and a technical path for lodging classification was established. This technical path can be used for wheat lodging monitoring, providing a scientific basis for agricultural production and improving agricultural production efficiency.Results and DiscussionsThe results showed that increasing the altitude of the UAV to 90 m significantly improved flight efficiency of wheat lodging areas. In comparison to flying at 30 m for the same monitoring range, data acquisition time was reduced to approximately 1/6th, and the number of photos needed decreased from 62 to 6. In terms of classification accuracy, the overall classification effect of SVM is better than that of RF and KNN. Additionally, when the image spatial resolution varied from 1.05 to 3.26 cm, the full feature set and all three optimized feature subsets had the highest classification accuracy at a resolution of 1.05 cm, which was better than at resolutions of 2.09 and 3.26 cm. As the image spatial resolution decreased, the overall classification effect gradually deteriorated and the positioning accuracy decreased, resulting in poor spatial consistency of the classification results. Further research has found that the Boruta-Shap feature selection method can reduce data dimensionality and improve computational speed while maintaining high classification accuracy. Among the three tested spatial resolution conditions (1.05, 2.09, and 3.26 cm), the combination of SVM and Boruta-Shap algorithms demonstrated the highest overall classification accuracy. Specifically, the accuracy rates were 95.6%, 94.6%, and 93.9% for the respective spatial resolutions. These results highlighted the superior performance of this combination in accurately classifying the data and adapt to changes in spatial resolution. When the image resolution was 3.26 cm, the overall classification accuracy decreased by 1.81% and 0.75% compared to 1.05 and 2.09 cm; when the image resolution was 2.09 cm, the overall classification accuracy decreased by 1.06% compared to 1.05 cm, showing a relatively small difference in classification accuracy under different flight altitudes. The overall classification accuracy at an altitude of 90 m reached 95.6%, with Kappa coefficient of 0.914, meeting the requirements for classification accuracy.ConclusionsThe study shows that the object-oriented SVM classifier and the Boruta-Shap feature optimization algorithm have strong application extension advantages in identifying lodging areas in remote sensing images at multiple flight altitudes. These methods can achieve high-precision crop lodging area identification and reduce the influence of image spatial resolution on model stability. This helps to increase flight altitude, expand the monitoring range, improve UAV operation efficiency, and reduce flight costs. In practical applications, it is possible to strike a balance between classification accuracy and efficiency based on specific requirements and the actual scenario, thus providing guidance and support for the development of strategies for acquiring crop lodging information and evaluating wheat disasters.
【Objective】 Most hilly regions in China are short of freshwater resources and recycling the effluent water in their drainage ditches is a way to relieve this pressure and improve water use efficiency. This paper investigates how reusing the effluent water for irrigation affects leaching of nitrogen (N) and phosphorus (P) from soils. 【Method】 In-situ experiment was set up in a field to measure the change in water flow and N and P concentrations in the ditches and the ditch buckets. We calculated the ratio of recycled water volume to the volume of water pumped for irrigation (i.e., regression rate), as well as the change in N and P pollutant loads and their determinants. 【Result】 The water had been drained and reused for irrigations for 24 cycles during the growing season, and the total regression rate reached 89.93%. The loads of total P, total N, nitrate nitrogen and ammonia nitrogen during the growing season were 0.28, 3.27, 2.35 and 2.35 kg/hm2, respectively. The load reductions of P and N were correlated with the ratio of their concentrations in the effluent and in the irrigation water. The reduction in total P and ammonia was significantly correlated with the regression rate. The reduction in total N and nitrate was significantly correlated with irrigation and rainfall in the second day after the irrigation. Nitrate reduction rate was also significantly correlated with temperature. 【Conclusion】 The cycles of drainage and its reuse for irrigation not only saves water but also improves utilization of water and fertilizers, thereby reducing the risk of N and P pollution to the downstream.
Agriculture (General), Irrigation engineering. Reclamation of wasteland. Drainage
Anantaa Kotal, Lavanya Elluri, Deepti Gupta
et al.
Big Data empowers the farming community with the information needed to optimize resource usage, increase productivity, and enhance the sustainability of agricultural practices. The use of Big Data in farming requires the collection and analysis of data from various sources such as sensors, satellites, and farmer surveys. While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as the privacy of the participants. Privacy regulations, such as the EU GDPR, the EU Code of Conduct on agricultural data sharing by contractual agreement, and the proposed EU AI law, have been created to address the issue of data privacy and provide specific guidelines on when and how data can be shared between organizations. To make confidential agricultural data widely available for Big Data analysis without violating the privacy of the data subjects, we consider privacy-preserving methods of data sharing in agriculture. Deep learning-based synthetic data generation has been proposed for privacy-preserving data sharing. However, there is a lack of compliance with documented data privacy policies in such privacy-preserving efforts. In this study, we propose a novel framework for enforcing privacy policy rules in privacy-preserving data generation algorithms. We explore several available agricultural codes of conduct, extract knowledge related to the privacy constraints in data, and use the extracted knowledge to define privacy bounds in a privacy-preserving generative model. We use our framework to generate synthetic agricultural data and present experimental results that demonstrate the utility of the synthetic dataset in downstream tasks. We also show that our framework can evade potential threats and secure data based on applicable regulatory policy rules.
Maurice Günder, Facundo Ramón Ispizua Yamati, Abel Andree Barreto Alcántara
et al.
Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case disease severity scoring for Cercospora Leaf Spot (CLS) in sugar beet. With concepts of Deep Label Distribution Learning (DLDL), special loss functions, and a tailored model architecture, we develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. One novelty in this work is the combination of remote sensing data with environmental parameters of the experimental sites for disease severity prediction. Although the model is evaluated on this special use case, it is held as generic as possible to also be applicable to various image-based classification and regression tasks. With our framework, it is even possible to learn models on multi-objective problems as we show by a pretraining on environmental metadata.
Galen Bergquist, Jessica Gutknecht, Craig Sheaffer
et al.
Intermediate wheatgrass (Thinopyrum intermedium (Host) Barkworth & D.R. Dewey; IWG) is a perennial sod-forming grass undergoing domesticated for use as a dual-use grain and forage crop with potential environmental benefits. IWG plant populations increase with stand age, which has been associated with reductions in grain yields after the second production year, thus management techniques are needed to maintain grain yields over time. We measured the effects of two between-row plant termination methods (cultivation and herbicide application) and two within-row suppression methods (burning and mowing), applied at different IWG physiological stages during the growing season. We measured IWG grain and straw yield, root biomass, and weed biomass. Treatments were initiated after the second year of grain harvest and applied for two consecutive years in southeast Minnesota. Grain yields were highest in production year 2 preceding any treatment application and declined in years 3 and 4 by 82% and 57% compared to year 2, respectively, across all management treatments. Termination methods reduced between-row IWG biomass and grain by up to 82% and 91% compared to the control but had no effect on within-row or total grain yield. Fall burning suppression treatments mitigated the negative effects of some termination treatments on grain yield and increased total straw yield. Spring mowing suppression treatments reduced grain and straw yield by 42% and 34%, respectively, compared to the control. Controls had minimal weed biomass while the termination treatments increased weed biomass, especially termination treatments that included herbicide application. No treatments sustained grain yields, but positive effects of some treatments were observed on total biomass and weeds and could be considered by growers.
The purpose of this study was to describe the development of a solar-powered submersible pump system without the use of batteries in agriculture. The submersible pump system used a solar drive to run it. The implementation uses a combination of solar trackers, water storage tanks, power converters, and stabilizers. The results of the study explained that solar trackers increased the efficiency of solar units that track the sun throughout the day and convert solar energy into DC electrical power.
The motivation for focusing on economic sanctions is the mixed evidence of their effectiveness. We assess the role of sanctions on the Russian international trade flow of agricultural products after 2014. We use a differences-in-differences model of trade flows data for imported and exported agricultural products from 2010 to 2020 in Russia. The main expectation was that the Russian economy would take a hit since it had lost its importers. We assess the economic impact of the Russian food embargo on agricultural commodities, questioning whether it has achieved its objective and resulted in a window of opportunity for the development of the domestic agricultural sector. Our results confirm that the sanctions have significantly impacted foodstuff imports; they have almost halved in the first two years since the sanctions were imposed. However, Russia has embarked on a path to reduce dependence on food imports and managed self-sufficient agricultural production.
This paper describes Fields2Cover, a novel open source library for coverage path planning (CPP) for agricultural vehicles. While there are several CPP solutions nowadays, there have been limited efforts to unify them into an open source library and provide benchmarking tools to compare their performance. Fields2Cover provides a framework for planning coverage paths, developing novel techniques, and benchmarking state-of-the-art algorithms. The library features a modular and extensible architecture that supports various vehicles and can be used for a variety of applications, including farms. Its core modules are: a headland generator, a swath generator, a route planner and a path planner. An interface to the Robot Operating System (ROS) is also supplied as an add-on. In this paper, the functionalities of the library for planning a coverage path in agriculture are demonstrated using 8 state-of-the-art methods and 7 objective functions in simulation and field experiments.